[en] Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.
Research center :
- Integrative Research Unit: Social and Individual Development (INSIDE) > PEARL Institute for Research on Socio-Economic Inequality (IRSEI)
Disciplines :
Social & behavioral sciences, psychology: Multidisciplinary, general & others Sociology & social sciences Public health, health care sciences & services
Author, co-author :
Leist, Anja ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC)
Klee, Matthias ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC)
Kim, Jung Hyun ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC)
Rehkopf, David
Bordas, Stéphane ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Muniz-Terrera, Graciela
Wade, Sarah
External co-authors :
yes
Language :
English
Title :
Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences
Publication date :
2022
Journal title :
Science Advances
ISSN :
2375-2548
Publisher :
American Association for the Advancement of Science (AAAS), Washington, United States - District of Columbia
Volume :
8
Pages :
eabk1942
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
European Projects :
H2020 - 803239 - CRISP - Cognitive Aging: From Educational Opportunities to Individual Risk Profiles
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